Weighting KL Divergence Loss:
To understand the effect of weighting KL Divergence loss, we assign different values to \(w_{KL}\)  such as 0.25, 0.5 and 1.0 and analyze the quality of sketches generated both visually and using the Ske-score. Unlike [1]}, where a higher \(w_{KL}\)  produces images closer to the data manifold, in our case, this behaviour is exactly opposite. Figure REF  shows the plot of \(L_{KL}\)  for different values of \(w_{KL}\)  while training the proposed model on cat sketches with GRU and LSTM respectively as the discriminators. The implication of this on the sketch generation is shown in Figure REF . Visually inspecting these sketches, we conclude that a value of 0.5 is ideal for \(w_{KL}\) .
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